Path tracking control is one of the most important functions for autonomous driving. In path tracking control, high accuracy and smooth tracking are required for safe and comfort driving. In order to meet these requirements, model predictive control approaches, which can obtain an optimized solution with respect to a predefined path, have been widely studied. Conventional predictive controllers have been studied based on a simple bicycle model. However, the conventional predictive controllers have a performance limitation in practical challenges due to the difference between the simple bicycle model and the actual vehicle. To overcome this limitation, the actuator dynamics of the steering system should be incorporated into the control design. In this paper, we propose a model predictive control based path tracking control algorithm to achieve the accurate and smooth tracking by incorporating the dynamic characteristics of the steering actuation system. In the proposed control algorithm, an optimal trajectory of the steering command is calculated by applying a quadratic programming optimization method. The proposed controller was verified by computer simulation with various driving scenarios. The simulation results show that the proposed controller can improve the tracking performance.
This study develops econometric models to predict the effect of access to and distance to public transit on automobile ownership and miles driven. Ordered logit model is used for automobile ownership and multiple regression model is used for vehicle miles traveled (VMT).
Inverse square root of transit distance is used as a measure for transit accessibility. Important findings in the analysis are (i) the number of licensed drivers is the primary determinant of the number of automobiles owned, (ii) the presence of children is not a significant factor in automobile ownership and VMT, and (iii) the VMT of multi‐vehicle households is more sensitive to transit than one‐vehicle households. Transit simulations are performed by improving the distance to and access to public transit. The results showed that total VMT in National Ambient Air Quality Standard non‐attained metropolitan statistical area is reduced by 11% (approximately 60 billion miles) with 0.1 miles simulation.
When optimizing a highway alignment, it is desirable to consider new and modified intersections along it. This article develops methods for locally optimizing intersections within highway alignment optimization processes. Design and operational characteristics for intersections are reviewed from the literature. The formulation considers the major costs that are sensitive to intersection characteristics. Genetic algorithms are used for optimal search. The proposed methods are implemented on an artificial study area and on a real one through the use of geographic information systems. The results show how the methods work for local optimization of intersections as well as for optimizing entire alignments. These methods can be used for improving search flexibility, thus allowing more effective intersections. They also provide a basis for extending the alignment optimization from single highways to networks.
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